Skill UI

Statsmodels is Python's premier library for rigorous statistical modeling. It provides comprehensive tools for estimation, inference, and diagnostics across a wide range of statistical methods. Use this skill for advanced analysis, including linear regression (OLS, WLS), generalized linear models (Logistic, Poisson), and complex time series forecasting (ARIMA). Ideal for econometric studies and producing publication-ready results.

This skill audits OpenMed NER or de-identification models to detect performance disparities across sensitive demographic subgroups (e.g., sex, age, race). It calculates metrics like per-group recall and leakage disparity, helping users identify instances of 'under-protection' before deployment, ensuring equitable AI governance and compliance.

This skill automates the creation of comprehensive Model Cards for OpenMed clinical NER or de-identification models. It gathers data from various OpenMed evaluation reports (e.g., `GateReport`, `fairness_report`, `error_report`) to structure critical governance information, including intended use, quantitative performance metrics, subgroup analysis, limitations, and required medical-device disclaimers. It ensures documentation is reproducible and compliant for clinical AI deployment.

An autonomous system designed to continuously improve code quality by scanning codebase metrics. It functions as an agent, proposing mechanical code changes, executing them, and committing only if defined metrics improve. It supports multiple modes (Scan, Scaffold, Run) for systematic, data-driven codebase refactoring.

This skill computes disproportionality metrics (PRR, ROR, EBGM, IC) using FAERS/OpenFDA drug-event data. It analyzes spontaneous reports to statistically screen for potential drug-reaction associations (Signal of Disproportionate Reporting, SDR). By constructing a 2x2 contingency table, it helps pharmacovigilance professionals identify over-reported adverse events, providing a statistical hypothesis for drug safety assessment.

A comprehensive assistant designed to analyze existing codebases for adherence to SOLID principles, common code smells, and complexity metrics. It identifies anti-patterns like tight coupling, excessive responsibility, and duplication, providing prioritized, actionable refactoring suggestions to improve code maintainability and quality.

This skill calculates and documents the residual re-identification risk of de-identified datasets. It goes beyond simple direct identifier removal by assessing the risk posed by quasi-identifiers (age, ZIP, dates). It computes structural metrics like k-anonymity and l-diversity, and runs empirical linkage attacks to provide a defensible memo supporting HIPAA Expert Determination for determining if a dataset is safe for release.

This tool analyzes SonarCloud quality issues for a specific pull request (PR). It fetches detailed metrics, including severity, issue type, and violation count, and generates a comprehensive, structured report. The output provides an executive summary, identifies top violating files and rules, and delivers a clear, actionable plan to guide developers toward merging high-quality code.

This skill transforms diverse raw source materials—such as articles, transcripts, and notes—into a highly structured summary suitable for a presentation or talk. It automatically detects the source type (Real-world Evidence or Conceptual Thesis), extracts the narrative arc, identifies key metrics, outlines main themes, and critically highlights potential gaps, providing a complete foundation for content creation.

This skill performs deep 'git archaeology' by cross-referencing commit history, changelogs, and initial summaries. It extracts verified metrics like commit velocity, contributor rankings, and release timelines, generating multiple detailed reports. Use this when building a REX talk and needing factual, data-backed evidence from a source git repository.

A systematic methodology for resolving discrepancies between conceptual models (documentation, tests, diagrams, metrics) and actual observed behavior (live code execution or real data). This process mandates that observed reality (the 'territory') always overrules the theoretical map, ensuring system integrity and accurate knowledge updates.

Structured GDPR Article 35 Data Protection Impact Assessment guidance based on EDPB criteria, Opinion 28/2024, and national blacklists. Assists threshold decisions, full assessment (assets, necessity, proportionality, risk tracks, mitigations), Art. 36 consultation, and multi-jurisdictional analysis for high-risk processing like profiling, AI, biometrics, large-scale monitoring.